prepare
01Command Claude Code
Familiarize yourse with the code in this repository and be ready to discuss it in detail.
Command Claude Code
Familiarize yourse with the code in this repository and be ready to discuss it in detail.
Command Claude Code
End-to-end test of the CLI on the dev environment. Run each step sequentially — stop and report if any step fails.
Command Claude Code
End-to-end test of publishing a skill with evals on the dev environment. Run each step sequentially — stop and report if any step fails.
Instructions file CodexOpenCode
Instructions for pymc-labs/decision-hub.
Instructions file
Instructions for pymc-labs/decision-hub, covering project overview, workspace structure, tech stack, development setup and environments (dev / prod / local).
Skill Claude CodeCodex
Describe what this skill does and how the agent should use it.
Skill Claude CodeCodex
Guide for using the dhub CLI — the AI skill manager for data science agents. Covers authentication, publishing, installing, running skills, managing API keys, eval reports, and troubleshooting. Use when users ask about dhub commands, skill publishing workflows, or need help with the Decision Hub CLI.
Skill Claude CodeCodex
Guide for creating effective skills for Claude Code agents. Covers skill design, implementation, validation, packaging, and optionally runtime environments and automated evaluations for Decision Hub publishing. Use when users want to create, improve, or package a skill.
Skill Claude CodeCodex
Describe what this skill does and how the agent should use it.
Skill Claude CodeCodex
Describe what this skill does and how the agent should use it.
pymc-labs/python-analytics-skills
Plugin Claude Code
Plugin marketplace listing 1 plugin: analytics.
pymc-labs/python-analytics-skills
Plugin Claude Code
Python analytics skills — PyMC and marimo.
pymc-labs/python-analytics-skills
Hook
Runs when you submit a prompt, before the agent sees it, executing suggest-skill.sh via bash. From pymc-labs/python-analytics-skills.
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load when the user is comparing Bayesian models, computing LOO-CV / ELPD, calling arvizstats.loo or arvizstats.compare, doing model stacking/averaging, or computing Bayes factors. Covers the ArviZ 1.1 LOO/ELPD/stacking APIs exclusively (no waic). Triggers include: model comparison, LOO, ELPD, compare, looexpectations…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load when the user is choosing priors, running prior predictive checks, calling findconstrainedprior, using PreliZ, or otherwise eliciting domain knowledge into a Bayesian model. Covers weakly informative priors, constrained priors, sensitivity analysis, and elicitation workflows. Triggers include: prior selection…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load when the user is working with pymc-extras (pmx) features: splines / BSplineBasis, distributional regression / GAMLSS, R2D2M2CP or horseshoe priors, discrete variable marginalization, or Laplace approximation via fitlaplace. Triggers include: pymcextras, pymc-extras, pmx, splines, BSplineBasis, distributional…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load whenever the user is working on code that imports pymc, pytensor, or arviz, or asks about Bayesian modeling, MCMC, priors, posteriors, sampling, or model diagnostics. Covers PyMC 6+, PyTensor 3+, ArviZ 1.1+ (DataTree API), pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Use for building probabilistic…
pymc-labs/python-analytics-skills
Skill Claude CodeCodex
Load when writing or modifying pytest tests that touch pymc.Model, pm.sample, or any PyMC model code. Covers pymc.testing.mocksample, pytest fixtures for Bayesian models, and the distinction between fast structure-only tests (mocking) and slow posterior inference tests. Triggers include: testing PyMC, pytest with…